{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NiftyNet provides a [\"CRF-RNN\" layer](https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/crf.py) for image segmentation, following the idea proposed in\n",
    "[Zheng et al., Conditional Random Fields as Recurrent Neural Networks, ICCV 2015](https://arxiv.org/abs/1502.03240).\n",
    "\n",
    "Different from many open-source implementations of the method, NiftyNet's version implements the core algorithm [Fast High‐Dimensional Gaussian Filtering](https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467-8659.2009.01645.x) with Numpy and TensorFlow APIs. One of the advantages is that the layer is ready-to-use -- once these common Python packages are (pip-)installed.\n",
    "\n",
    "This tutorial demonstrates the basic usage of this layer.\n",
    "\n",
    "This demo requires two files, a CT image `100_CT.nii` and a 'noisy' logits map `100__niftynet_out.nii.gz`, to be placed in `demo/crf_as_rnn` folder.\n",
    "The files (~100Mb) can be downloaded from https://www.dropbox.com/s/lf1hvfyvuo9lsc1/demo_prob.tar.gz?dl=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CRF inferences\n",
    "Given a CRF model and some noisy segmentation outputs, model inference is to find the best underlying 'true' segmentation label that minimises CRF's energy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load and visualise the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "niftynet_path = '/Users/demo/Documents/NiftyNet/'\n",
    "sys.path.insert(0, niftynet_path)\n",
    "\n",
    "from niftynet.layer.crf import CRFAsRNNLayer\n",
    "\n",
    "import nibabel as nib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CT image shape: (144, 144, 144)\n",
      "Predicted logits shape: (144, 144, 144, 1, 9)\n"
     ]
    }
   ],
   "source": [
    "ct_image = nib.load('100_CT.nii').get_data()\n",
    "logits = nib.load('100__niftynet_out.nii.gz').get_data()\n",
    "\n",
    "print('CT image shape: {}'.format(ct_image.shape))\n",
    "print('Predicted logits shape: {}'.format(logits.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CT volume has 144x144x144 voxels,\n",
    "the predicted logits has nine channels corresponding\n",
    "to eight types of organs (plus a channel for background).\n",
    "As a demo, let's only study a slice of the volume:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CT slice shape: (144, 144)\n",
      "Predicted logits shape: (144, 144, 9)\n"
     ]
    }
   ],
   "source": [
    "slice_idx = 73\n",
    "ct_slice = np.transpose(ct_image[::-1, ::-1, slice_idx])\n",
    "ct_logits = np.transpose(logits[::-1, ::-1, slice_idx, 0, :], axes=(1, 0, 2))\n",
    "\n",
    "print('CT slice shape: {}'.format(ct_slice.shape))\n",
    "print('Predicted logits shape: {}'.format(ct_logits.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualisation of the slice:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x24ed930f0>"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2547c2cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 2, figsize=(10,5))\n",
    "axes[0].imshow(ct_slice, cmap='gray')\n",
    "axes[1].imshow(np.argmax(ct_logits, -1), cmap='Accent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Build the graph and initialise a CRFAsRNNLayer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CRFAsRNNLayer requires two inputs: \n",
    "\n",
    "-- image features `[batch x spatial_dims x n_features]`\n",
    "\n",
    "-- initial segmentation logits`[batch x spatial_dims x n_classes]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensor(\"ExpandDims_24:0\", shape=(1, 144, 144, 9), dtype=float32)\n",
      "Tensor(\"ExpandDims_26:0\", shape=(1, 144, 144, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "# make a tensor with batch size 1 and channel 9\n",
    "tf_logits = tf.constant(ct_logits, dtype=tf.float32)\n",
    "tf_logits = tf.expand_dims(tf_logits, axis=0)\n",
    "print(tf_logits)\n",
    "\n",
    "# make a tensor of the CT intensity\n",
    "tf_features = tf.constant(ct_slice, dtype=tf.float32)\n",
    "tf_features = tf.expand_dims(tf_features, axis=0)\n",
    "tf_features = tf.expand_dims(tf_features, axis=-1)\n",
    "print(tf_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [],
   "source": [
    "crf_layer = CRFAsRNNLayer(alpha=160., beta=3., gamma=3., T=5,\n",
    "                          w_init=[1.0 * np.ones(9), 3.0 * np.ones(9)])\n",
    "smoothed_logits = crf_layer(tf_features, tf_logits)\n",
    "smoothed_label = tf.cast(tf.argmax(smoothed_logits, -1), tf.int32)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    output_label = sess.run(smoothed_label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualise the outputs:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x139919da0>"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GLAXulfQE2czSd0k6iAFmnfZU9GbtNHAoRMT9EfG7EbEkIpaQDRGOiogdZLNOfyLfC3EM8KuImC63ZDOrUj+7JK8GbgUOl7RN0qrdrH4jsBXYAvwL8DelVGlmtRl21unu25d0XQ7gzOJlmVlTlL2PGy5Cehb4P+DnTdcyiwNoX11trAlc16DqruutEXHgXCu1IhQAJN0REcubrmOmNtbVxprAdQ2qrXX5uw9mlnAomFmiTaFwadMF9NDGutpYE7iuQbWyrtZsUzCzdmhTp2BmLdB4KEg6QdJP83MwrGmwjkMl3SzpIUkPSjo7X36hpO2S7sl/Tmqgtick3Z8//x35sv0kbZD0WP5735prOrzrNblH0vOSzmni9ZrtnB+9Xp+6zvnRo6ZvSnokf97vS1qYL18i6cWu1+w7VdTUt4ho7AeYBzwOHAbsCdwLHNFQLVNkh2sDvBl4FDgCuBD4QsOv0xPAATOW/R2wJr+8Brio4X/HHcBbm3i9gPcCRwEPzPX6ACcBPwQEHANsrrGmDwJ75Jcv6qppSfd6Tf803SkcDWyJiK0R8TJwDdk5GWoXEdMRcVd++dfAw7T7a98rgHX55XXAhxus5Xjg8Yh4soknj9nP+dHr9anlnB+z1RQRN0XErvzqbWRfGGydpkOhledfkLQEOBLYnC/6TN7yXV53m54L4CZJd+ZfOQdYFL/9stkOYFEDdXWsBK7uut706wW9X5+2/J/7FFnH0rFU0t2SbpH0Jw3U85qmQ6F1JL0J+B5wTkQ8T3ZKubcBfwxMA//QQFnviYijyE53d6ak93bfGFkP2shuJEl7AqcA1+WL2vB6JZp8fWYj6QJgF3BVvmga+L2IOBL4HPDvkt7SVH1Nh0Lf51+og6Q3kgXCVRFxA0BEPBMRr0bEb8i++Xl03XVFxPb8907g+3kNz3Ta3vz3zrrryp0I3BURz+Q1Nv565Xq9Po3+n5P0SeBk4GN5WBERL0XEL/LLd5JtZ3tHXTXN1HQo3A4sk7Q0/8RZSXZOhtpJEnAZ8HBEfKtrefd488+B153VuuK69pH05s5lso1VD5C9Tqfnq50O/KDOurqcRtfQoenXq0uv16exc35IOoHsvKanRMQLXcsPlDQvv3wY2YmPt9ZR06ya3tJJtjX4UbJ0vKDBOt5D1mLeB9yT/5wE/Btwf758PTBVc12Hke2VuRd4sPMaAfsDG4HHgB8D+zXwmu0D/AJY0LWs9teLLJSmgVfIthGs6vX6kO11+Of8/9v9wPIaa9pCtj2j8//rO/m6f5H/294D3AV8qO5/y+4fH9FoZommhw9m1jIOBTNLOBTMLOFQMLOEQ8HMEg4FM0s4FMws4VAws8T/A9I/cK3DtptdAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x254981748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(output_label[0,...], cmap='Accent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Effects of weighting of the kernels\n",
    "The pairwise potential of Dense CRF consists of two kernels:\n",
    "\n",
    "-- bilateral kernel, encourages nearby pixels with similar features/colours to have the same label.\n",
    "\n",
    "-- spatial kernel, encourages nearby pixels to have the same label.\n",
    "\n",
    "To see how they change the final segmentation output, we first define a wrapper to compute the output given specific weights, and then run the layer with different weight combinations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "def varying_w_init(w_bilateral=1.0, w_spatial=3.0):\n",
    "    crf_layer = CRFAsRNNLayer(alpha=5., beta=5., gamma=3., T=5,\n",
    "                              w_init=[w_bilateral * np.ones(9), w_spatial * np.ones(9)])\n",
    "    smoothed_logits = crf_layer(tf_features, tf_logits)\n",
    "    smoothed_label = tf.cast(tf.argmax(smoothed_logits, -1), tf.int32)\n",
    "\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        output_label = sess.run(smoothed_label)\n",
    "    return output_label[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualise the effects of using spatial kernels only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = []\n",
    "w_spatials = [0.0, 1.0, 5.0, 10.0, 100.0, 200.0]\n",
    "for w_spatial in w_spatials:\n",
    "    labels.append(varying_w_init(0.0, w_spatial))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25ea49e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(2, 4, figsize=(15,7))\n",
    "axes[0][0].imshow(ct_slice, cmap='gray'); \n",
    "axes[0][0].set_title('CT'); axes[0][0].set_axis_off()\n",
    "axes[0][1].imshow(np.argmax(ct_logits, -1), cmap='Accent'); \n",
    "axes[0][1].set_title('Initial seg'); axes[0][1].set_axis_off()\n",
    "for idx, label in enumerate(labels):\n",
    "    i = idx + 2\n",
    "    c_axes = axes[i//4][i%4]\n",
    "    c_axes.imshow(label, cmap='Accent'); \n",
    "    c_axes.set_title('spatial_w = {}'.format(w_spatials[idx]))\n",
    "    c_axes.set_axis_off()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualise the effects of using bilateral kernels only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = []\n",
    "w_bilaterals = [0.0, 1.0, 5.0, 10.0, 100.0, 200.0]\n",
    "for w_bilateral in w_bilaterals:\n",
    "    labels.append(varying_w_init(w_bilateral, 0.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26f5e1ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(2, 4, figsize=(15,7))\n",
    "axes[0][0].imshow(ct_slice, cmap='gray'); \n",
    "axes[0][0].set_title('CT'); axes[0][0].set_axis_off()\n",
    "axes[0][1].imshow(np.argmax(ct_logits, -1), cmap='Accent'); \n",
    "axes[0][1].set_title('Initial seg'); axes[0][1].set_axis_off()\n",
    "for idx, label in enumerate(labels):\n",
    "    i = idx + 2\n",
    "    c_axes = axes[i//4][i%4]\n",
    "    c_axes.imshow(label, cmap='Accent'); \n",
    "    c_axes.set_title('bilateral_w = {}'.format(w_bilaterals[idx]))\n",
    "    c_axes.set_axis_off()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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